HMR-ODTA: Online Diverse Task Allocation for a Team of Heterogeneous Mobile Robots

TL;DR

This paper introduces HMR-ODTA, a decentralized online scheduling algorithm for heterogeneous robot teams to efficiently handle diverse, time-sensitive pickup and delivery tasks, significantly reducing late penalties.

Contribution

It presents a novel dynamic scheduling framework for heterogeneous robots that supports online rescheduling and diverse task handling, improving delivery timeliness.

Findings

Penalties reduced by up to 63% in simulations.

Algorithm effectively manages dynamic task rescheduling.

Improves task completion rates in structured environments.

Abstract

Coordinating time-sensitive deliveries in environments like hospitals poses a complex challenge, particularly when managing multiple online pickup and delivery requests within strict time windows using a team of heterogeneous robots. Traditional approaches fail to address dynamic rescheduling or diverse service requirements, typically restricting robots to single-task types. This paper tackles the Multi-Pickup and Delivery Problem with Time Windows (MPDPTW), where autonomous mobile robots are capable of handling varied service requests. The objective is to minimize late delivery penalties while maximizing task completion rates. To achieve this, we propose a novel framework leveraging a heterogeneous robot team and an efficient dynamic scheduling algorithm that supports dynamic task rescheduling. Users submit requests with specific time constraints, and our decentralized algorithm, Heterogeneous Mobile Robots Online Diverse Task Allocation (HMR-ODTA), optimizes task assignments to ensure timely service while addressing delays or task rejections. Extensive simulations validate the algorithm's effectiveness. For smaller task sets (40-160 tasks), penalties were reduced by nearly 63%, while for larger sets (160-280 tasks), penalties decreased by approximately 50%. These results highlight the algorithm's effectiveness in improving task scheduling and coordination in multi-robot systems, offering a robust solution for enhancing delivery performance in structured, time-critical environments.